Rapid estimation of wood density and total extractives of two important Dalbergia species using near-infrared (NIR) spectroscopy and multivariate analysis.
Saved in:
| Title: | Rapid estimation of wood density and total extractives of two important Dalbergia species using near-infrared (NIR) spectroscopy and multivariate analysis. |
|---|---|
| Authors: | Deepa, M. S.1 (AUTHOR), Shukla, S. R.1 (AUTHOR) shuklasr@gmail.com |
| Source: | Wood Material Science & Engineering. Dec2025, Vol. 20 Issue 6, p1288-1303. 16p. |
| Subjects: | Wood density, Near infrared spectroscopy, Multivariate analysis, Plant species, Timber, Prediction models, Bioactive compounds, Renewable natural resources |
| Abstract: | Wood density and chemical extractives are important parameters influencing timber quality and utility in wood industry. Several wood properties, particularly mechanical parameters and durability, depend respectively on density and extractive contents. This study aimed to apply the near-infrared (NIR) spectroscopy technique for rapid and accurate estimation of density and extractive contents of Dalbergia latifolia and D. sissoo wood species. The objective was to develop a predictive model for properties, addressing the critical gap in efficient and large-scale assessment techniques for wood industries. Density and extractive contents were determined by conventional and wet chemistry methods. Spectral data of woods were subjected to multivariate analysis to improve the accuracy of calibration models and performance was assessed using coefficient of determination (R²) and root mean square error of prediction. The optimised models exhibited good predictive accuracy with high R²of 0.85 and 0.82 for density and extractive contents respectively. Spectral patterns correlating density and extractive variations identified provided valuable insights into the chemical composition and density relationship in Dalbergia species. The study thus highlights the potential of this non-invasive analytical approach in assessing the quality parameters, which would facilitate sustainable utilisation and contribute to the advancement of woodworking industries. [ABSTRACT FROM AUTHOR] |
| Copyright of Wood Material Science & Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 189685948 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Rapid estimation of wood density and total extractives of two important Dalbergia species using near-infrared (NIR) spectroscopy and multivariate analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Deepa%2C+M%2E+S%2E%22">Deepa, M. S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shukla%2C+S%2E+R%2E%22">Shukla, S. R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shuklasr@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Wood+Material+Science+%26+Engineering%22">Wood Material Science & Engineering</searchLink>. Dec2025, Vol. 20 Issue 6, p1288-1303. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wood+density%22">Wood density</searchLink><br /><searchLink fieldCode="DE" term="%22Near+infrared+spectroscopy%22">Near infrared spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Multivariate+analysis%22">Multivariate analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+species%22">Plant species</searchLink><br /><searchLink fieldCode="DE" term="%22Timber%22">Timber</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Bioactive+compounds%22">Bioactive compounds</searchLink><br /><searchLink fieldCode="DE" term="%22Renewable+natural+resources%22">Renewable natural resources</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Wood density and chemical extractives are important parameters influencing timber quality and utility in wood industry. Several wood properties, particularly mechanical parameters and durability, depend respectively on density and extractive contents. This study aimed to apply the near-infrared (NIR) spectroscopy technique for rapid and accurate estimation of density and extractive contents of Dalbergia latifolia and D. sissoo wood species. The objective was to develop a predictive model for properties, addressing the critical gap in efficient and large-scale assessment techniques for wood industries. Density and extractive contents were determined by conventional and wet chemistry methods. Spectral data of woods were subjected to multivariate analysis to improve the accuracy of calibration models and performance was assessed using coefficient of determination (R²) and root mean square error of prediction. The optimised models exhibited good predictive accuracy with high R²of 0.85 and 0.82 for density and extractive contents respectively. Spectral patterns correlating density and extractive variations identified provided valuable insights into the chemical composition and density relationship in Dalbergia species. The study thus highlights the potential of this non-invasive analytical approach in assessing the quality parameters, which would facilitate sustainable utilisation and contribute to the advancement of woodworking industries. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Wood Material Science & Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=189685948 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/17480272.2024.2394609 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1288 Subjects: – SubjectFull: Wood density Type: general – SubjectFull: Near infrared spectroscopy Type: general – SubjectFull: Multivariate analysis Type: general – SubjectFull: Plant species Type: general – SubjectFull: Timber Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Bioactive compounds Type: general – SubjectFull: Renewable natural resources Type: general Titles: – TitleFull: Rapid estimation of wood density and total extractives of two important Dalbergia species using near-infrared (NIR) spectroscopy and multivariate analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Deepa, M. S. – PersonEntity: Name: NameFull: Shukla, S. R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 17480272 Numbering: – Type: volume Value: 20 – Type: issue Value: 6 Titles: – TitleFull: Wood Material Science & Engineering Type: main |
| ResultId | 1 |
